Natural Language Processing for Plagiarism Checker

What is Natural Language Processing?

Artificial intelligence serves as a blessing for plagiarism checker. Natural language processing is a process to detect plagiarism hidden within. Natural Language Processing or NLP is the way to extract materials from the raw and unconstructed data.

The NLP process can make the whole data complex, and the supervised process is said to be the most used NLP process, among the other. The varied kinds of NLP processes have been explained and elaborated below:

  • NLP based on Semantic analysis: this is a process used to detect plagiarism between two words or more and whether they are near in meaning with each other or semantically same. After comparing it gets deduced, the smaller the value, the more is the similarity between the words.
  • NLP based on Lexical analysis: the method detects plagiarism involving the structure and grammar usage in a sentence. In any NLP, the selected text gets divided into tokens or words, while searching for similarity or dissimilarity in the text. Structural copying detected and besides flaws in structures are also pointed, and necessary changes are done well ahead. However, this process has its drawbacks and is a bit imperfect. The disadvantage is that it analyzes only small sentences. 
  • NLP based on Syntactic analysis: similar to any other NLP after the breakdown of the sentences into tokens, each portion is compared with the grammar or vocabulary used. After that, the final decision depends on whether the words are used correctly and are grammatically error-free.

The final nod is given only after studying the decision tree provided by the structural scaling of the sentences. A machine learning algorithm for structural analysis is as follows:

  1. Top-down parsing- it starts with the sentence then comes down to the paraphrasing of a noun phrase and verb phrase.
  2. Bottom-up parsing- in contrast to the above one here, the parsing starts with the first word and then emerges each sentence to form a tree-like structure. 
  3. Depth parsing- it searches for the deepest node or the basic unit before plagiarism check and then reaches out to the larger areas of the tree. 
  4. Repeated programming parsing: it only reduces the discrepancy, if any, in the article and resolves all ambiguities to help present the article efficiently.
  5. Dynamic programming: it is a just a partial form of repeated parsing, used by some plagiarism checkers as a tool.
  • NLP on grammar analyzing: The framing and rephrasing of a particular sentence can be done by copying it in exact from the source. The mentioned process analyzes the same with the algorithm by breaking the structure into smaller units. Each structure or unit gets compared with the grammars provided and whether they are correct or just a copy-paste from another original piece. 

The cumulative above discussion all in all aims towards a presentation of an original piece. Plagiarism, whether intentional or not, can never be encouraged. All the mentioned types of natural language processing methods check and recheck the use of language, grammar, and others. for the improvisation of the piece. 

Machine learning algorithm deduces the human language by coding-decoding the provided document in smaller units and ease out the process of plagiarism check. It is necessary because specific minutes, hidden language plagiarism can get overlooked by the reader. 

It is impossible to detect every possible error present. For grammatical and syntactical errors, the process gets completed in a short span, and for the rest part i.e., to check to copy from source NLP completes it perfectly.

What history says about Natural Language Processing?

The invention of the algorithm method dates back to about 1950. Alan Turing discovered it, to emphasize more on the use of computer language for correctional methods. Further improvisation was done by Georgetown experiment that involved full translation of the Russian language into English.

However, gradually, the funding for translation machine intelligence was dramatically reduced. Despite the obstacles, people understood the need for developing artificial intelligence in a more useful way, since plagiarism was a problem that is today having a disruptive effect on academic and research papers. The need to develop something called an online plagiarism checker sprung up from this crisis. 

The idea got a gradual lease of life after the 1980s when the Turing method got implemented in language correction. The age-old grammatical rules, when implemented in machine language, developed several difficulties.

The process, too, became quite lofty. Hence, the developers processed the algorithmic calculation in such a way that it sounds easier even for the computer. Hence, the results are as per the wish of the reader. 

In 2010 deep neural learning was put to use. It involves a deeper deduction and searches for language processing. Further, the learning process is delved deep to specialize the possible flaws present in the piece of writing. 

Using Reinforcement Learning for NLP

In order to understand how reinforcement learning or RL gets used for NLP, one first needs to understand what reinforcement learning is. Well, in RL the behavioral psychology is used on the software agent.

The trial and error method ensure that the software agents learn a particular kind of behavior over a period and increase the cumulative reward in a particular environment. Now that one knows the theory behind this particular learning, it becomes easier to show how it gets used for NLP.

Often people working with machine learning and NLP feel that RL is perfect for NLP because, in the case of NLP, the system is in the process of learning the behavior of that of the trainer. The simulated ambiance plays an important role here, where the trial and error method, too, has a critical part to play.

Now in order to understand this, a particular example can be used here. Suppose during the classification of text process, where the data is there from varied domains, and there is not any training data, an environment and an agent gets created. 

The agent tries to classify the text from the data, and in the beginning, it uses some arbitrary methods. After receiving the results of its action, the agent can now decide for the next step.

Types of plagiarism and its detection:

The use of the internet is a blessing and a curse for the common mass. Crimes of several dimensions get disclosed through the internet. One such type is plagiarism. It involves the copying or theft of an original idea and publishing the same claiming to be one’s own.

The hard work and creativity of another person cannot get copied so smoothly. It is an immoral act for the person who demands to showcase himself as a creative writer. Plagiarism is divided further into two types, namely, intrinsic and extrinsic.

  • Know the Difference between Intrinsic and Extrinsic Plagiarism

Today, plagiarism broadly gets classified into extrinsic and intrinsic plagiarism. When it comes to the topic of plagiarism checking, then the main job that the plagiarism checking tools are carrying out is pointing out extrinsic plagiarism

In other words, it is just a cursory check on a particular content wherein the intricate details such as grammar, parts-of-speech, and other things often get overlooked. The matter gets delivered over the internet, along with those flaws. However, with advanced technologies like natural language processing coming into the picture, such scenarios can very well be handled.

The outward or extrinsic plagiarism is comparatively easy to be detected, while the intrinsic plagiarism is quite hard. The use of machine intelligence is vital here. It can detect what type of intrinsic plagiarism gets used in the selected piece.

  1. Near copies, intrinsic plagiarism is a type where a thin line of differentiation between the selected text for plagiarism detection and the source. It is unauthorized and unethical not to acknowledge the borrowed idea.
  2. Disguised plagiarism is restructuring a copied idea to avoid plagiarism detection. 
  3. Translated intrinsic plagiarism is a type quite clever one. It is the translation of a previously used idea in a foreign language, translated in the vernacular language, and copied. 
  4. The idea is that genre of plagiarism that discusses precisely the same topic with a change in structure and use of words. 

The Reasons behind the Increase of this Tendency

The tendency and habit of plagiarism need immediate control. It has increased due to several reasons like lethargy, easy access to the internet, and others. Instead of taking the help of the internet as a blessing, the misuse increases with days.

Students and academicians lookout for papers and ideas already developed and uploaded in the net and end up copying the same. Academic papers, therefore, most of the time, fails to emanate new and improvised ideas on topics known to all. 

The hunger for the search of an underlying truth behind any known topic which is lying latent involves deep learning of the subject.

On the other hand, even supervisors are partial. They become bored with the checking of the written piece of each student under him. This laziness results in an unsupervised learning and further production of demeaning quality of papers. 

So, in every attempt to publish a blog or piece on plagiarism, it is highlighted, and stress is there on the fact that improper guidance will only increase the problem of plagiarism rather than decreasing it.

How can NLP help in Plagiarism Detection?

The paid or the free plagiarism checkers for students help in detecting the duplicate content from the original content. These checkers help in identifying a similar text, and for that, they use the unique identifier or the structural patterns. 

NLP acts as an essential link between computer language and human language, and the same when blended with the machine and deep learning lead to excellent outcomes such as the one that gets implemented in the development of Chatbots.

Now coming to how this gets used in checking plagiarism, NLP makes use of algorithms to check plagiarism. Now, the question is, how does this algorithm work in order to put a check on plagiarism? 

A straightforward way to put it is by parsing or breaking sentences into bits or tokens and processing the same in pieces. It follows a popular method that is known as ‘Latent Semantic Analysis’ or ‘LSA.’

How LSA helps in Plagiarism Checking?

LSA has a very scientific approach towards NLP based plagiarism checking. In other words, it analyses to what extent two words are similar with the help of cosine values of the vectors being reproduced by the words that are in the radar of comparison. 

The proximity of the values leads to a conclusion about the similarity between the words. The process may sound pretty straightforward, but in reality, the application of NLP in plagiarism checking involves a lot of mathematical and statistical calculations involving ‘Lexical Analysis, Syntactic Analysis,’ and even a much-refined approach of the algorithm with particular emphasis on grammar. 

The Other Algorithms of NLP

Apart from these, there are other algorithms in NLP as well, such as ‘MinHash or Locality-sensitive Hashing, SimHash and Text Profile Signature’ that use even better scientific techniques of checking plagiarism. 

However, the basic approach is all about breaking and checking sentences first with the words, and then finally, the main idea gets portrayed in the matter. 

The plagiarism check based on NLP may also act as a refinement tool for the content as this process removes stop-words or words that are burdening the data without adding any value in a sentence. 

So, in a way, NLP can play a pivotal role in the field of plagiarism checking and protection of intellectual property rights in the future days to come.

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